mkdocs-nbsync

🔄 Stop fighting with notebook documentation!

The Problem: Screenshots break. Exports get forgotten. Code and docs drift apart. 😩

The Solution: One simple syntax that keeps everything in sync:

![My awesome plot](notebook.ipynb){#figure-name}

That's it! No more manual exports. No more broken documentation. 🎉

mkdocs-nbsync is a MkDocs plugin that seamlessly embeds Jupyter notebook visualizations in your documentation, solving the disconnect between code development and documentation.

Why Use mkdocs-nbsync?

The Documentation Challenge

Data scientists, researchers, and technical writers face a common dilemma:

• Development happens in notebooks - ideal for experimentation and visualization
• Documentation lives in markdown - perfect for narrative and explanation
• Connecting the two is painful - screenshots break, exports get outdated

Our Solution

This plugin creates a live bridge between your notebooks and documentation by:

• Keeping environments separate - work in the tool best suited for each task
• Maintaining connections - reference specific figures from notebooks
• Automating updates - changes to notebooks reflect in documentation

Key Benefits

• True Separation of Concerns: Develop visualizations in Jupyter notebooks and write documentation in markdown files, with each tool optimized for its purpose.

• Intuitive Markdown Syntax: Use standard image syntax with a simple extension to reference notebook figures: ![alt text](notebook.ipynb){#figure-id}

• Automatic Updates: When you modify your notebooks, your documentation updates automatically in MkDocs serve mode.

• Clean Source Documents: Your markdown remains readable and focused on content, without code distractions or complex embedding techniques.

• Enhanced Development Experience: Take advantage of IDE features like code completion and syntax highlighting in the appropriate environment.

Quick Start

1. Installation

pip install mkdocs-nbsync

2. Configuration

Add to your mkdocs.yml:

plugins:
  - mkdocs-nbsync:
      src_dir: ../notebooks

3. Mark Figures in Your Notebook

In your Jupyter notebook, identify figures with a comment:

# #my-figure
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 4))
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])

4. Reference in Markdown

Use standard Markdown image syntax with the figure identifier:

![Chart description](my-notebook.ipynb){#my-figure}

The Power of Separation

Creating documentation and developing visualizations involve different workflows and timeframes. When building visualizations in Jupyter notebooks, you need rapid cycles of execution, verification, and modification.

This plugin is designed specifically to address these separation of concerns, allowing you to:

• Focus on code in notebooks without documentation distractions
• Focus on narrative in markdown without code interruptions
• Maintain powerful connections between both environments

Each environment is optimized for its purpose, while the plugin handles the integration automatically.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.